{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/qiskit/providers/ibmq/ibmqfactory.py:192: UserWarning: Timestamps in IBMQ backend properties, jobs, and job results are all now in local time instead of UTC.\n",
      "  warnings.warn('Timestamps in IBMQ backend properties, jobs, and job results '\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Importing standard Qiskit libraries and configuring account\n",
    "from qiskit import QuantumCircuit, execute, Aer, IBMQ\n",
    "from qiskit.compiler import transpile, assemble\n",
    "from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "# Loading your IBM Q account(s)\n",
    "provider = IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 11 - Ignis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import plot and math libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import the noise models and some standard error methods\n",
    "from qiskit.providers.aer.noise import NoiseModel\n",
    "from qiskit.providers.aer.noise.errors.standard_errors import amplitude_damping_error, phase_damping_error\n",
    "\n",
    "# Import all three coherence circuits generators and fitters \n",
    "from qiskit.ignis.characterization.coherence import t1_circuits, t2_circuits, t2star_circuits\n",
    "from qiskit.ignis.characterization.coherence import T1Fitter, T2Fitter, T2StarFitter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of gates per test circuit: \n",
      " [   1   10   19   28   37   46   55   64   73   82   91  100  200  400\n",
      "  800 1000 2000 4000] \n",
      "\n",
      "Delay times for each test circuit created, respectively:\n",
      " [1.0e-01 1.0e+00 1.9e+00 2.8e+00 3.7e+00 4.6e+00 5.5e+00 6.4e+00 7.3e+00\n",
      " 8.2e+00 9.1e+00 1.0e+01 2.0e+01 4.0e+01 8.0e+01 1.0e+02 2.0e+02 4.0e+02]\n"
     ]
    }
   ],
   "source": [
    "# Generate the T1 test circuits\n",
    "\n",
    "# Generate a list of number of gates to add to each circuit\n",
    "# using np.linspace so that the number of gates increases linearly \n",
    "# and append with a large span at the end of the list (200-4000)\n",
    "num_of_gates = np.append((np.linspace(1, 100, 12)).astype(int), np.array([200, 400, 800, 1000, 2000, 4000]))\n",
    "\n",
    "#Define the gate time for each Identity gate\n",
    "gate_time = 0.1\n",
    "\n",
    "# Select the first qubit as the one we wish to measure T1\n",
    "qubits = [0]\n",
    "\n",
    "# Generate the test circuits given the above parameters\n",
    "test_circuits, delay_times = t1_circuits(num_of_gates, gate_time, qubits)\n",
    "\n",
    "# The number of I gates appended for each circuit\n",
    "print('Number of gates per test circuit: \\n', num_of_gates, '\\n')\n",
    "\n",
    "# The gate time of each circuit (number of I gates * gate_time)\n",
    "print('Delay times for each test circuit created, respectively:\\n', delay_times)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total test circuits created:  18\n",
      "Test circuit 1 with 1 Identity gate:\n"
     ]
    },
    {
     "data": {
      "image/png": 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IiEgWS4KIiGSxJIiISBZLgoiIZP0XJhtS5wwbHzcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 501.687x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Total test circuits created: ', len(test_circuits))\n",
    "print('Test circuit 1 with 1 Identity gate:')\n",
    "test_circuits[0].draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test circuit 2 with 10 Identity gates:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1585.29x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Test circuit 2 with 10 Identity gates:')\n",
    "test_circuits[1].draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the simulator with amplitude damping noise\n",
    "\n",
    "# Set the amplitude damping noise channel parameters T1 and Lambda\n",
    "t1 = 20\n",
    "lam = np.exp(-gate_time/t1)\n",
    "\n",
    "# Generate the amplitude dampling error channel\n",
    "error = amplitude_damping_error(1 - lam)\n",
    "noise_model = NoiseModel()\n",
    "\n",
    "# Set the dampling error to the ID gate on qubit 0.\n",
    "noise_model.add_quantum_error(error, 'id', [0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the simulator with the generated noise model\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "shots = 200\n",
    "backend_result = execute(test_circuits, backend, shots=shots, noise_model=noise_model).result()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the noisy results of the largest (last in the list) circuit\n",
    "plot_histogram(backend_result.get_counts(test_circuits[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the noisy results of the largest (last in the list) circuit\n",
    "plot_histogram(backend_result.get_counts(test_circuits[len(test_circuits)-1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the parameters for the T1Fitter, A, T1, and B\n",
    "param_t1 = t1*1.2\n",
    "param_a = 1.0\n",
    "param_b = 0.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'$T_1$ for qubit 0'}, xlabel='Time [micro-seconds]', ylabel='Excited State Population'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate the T1Fitter for our test circuit results\n",
    "fit = T1Fitter(backend_result, delay_times, qubits,\n",
    "               fit_p0=[param_a, param_t1, param_b],\n",
    "               fit_bounds=([0, 0, -1], [2, param_t1*2, 1]))\n",
    "\n",
    "# Plot the fitter results for T1 over each test circuit's delay time\n",
    "fit.plot(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the thermal relaxation error we will use to create our error\n",
    "from qiskit.providers.aer.noise.errors.standard_errors import thermal_relaxation_error\n",
    "\n",
    "# Import the T2Fitter Class and t2_circuits method\n",
    "from qiskit.ignis.characterization.coherence import T2Fitter\n",
    "from qiskit.ignis.characterization.coherence import t2_circuits\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of gates per test circuit: \n",
      " [  1   7  13  19  25  31  37  43  49  55  62  68  74  80  86  92  98 104\n",
      " 110 116 123 129 135 141 147 153 159 165 171 177 184 190 196 202 208 214\n",
      " 220 226 232 238 245 251 257 263 269 275 281 287 293 300] \n",
      "\n",
      "Delay times for T2 echo test circuits:\n",
      " [ 0.2  1.4  2.6  3.8  5.   6.2  7.4  8.6  9.8 11.  12.4 13.6 14.8 16.\n",
      " 17.2 18.4 19.6 20.8 22.  23.2 24.6 25.8 27.  28.2 29.4 30.6 31.8 33.\n",
      " 34.2 35.4 36.8 38.  39.2 40.4 41.6 42.8 44.  45.2 46.4 47.6 49.  50.2\n",
      " 51.4 52.6 53.8 55.  56.2 57.4 58.6 60. ]\n"
     ]
    }
   ],
   "source": [
    "num_of_gates = (np.linspace(1, 300, 50)).astype(int)\n",
    "gate_time = 0.1\n",
    "\n",
    "# Note that it is possible to measure several qubits in parallel\n",
    "qubits = [0]\n",
    "\n",
    "t2echo_test_circuits, t2echo_delay_times = t2_circuits(num_of_gates, gate_time, qubits)\n",
    "\n",
    "# The number of I gates appended for each circuit\n",
    "print('Number of gates per test circuit: \\n', num_of_gates, '\\n')\n",
    "\n",
    "# The gate time of each circuit (number of I gates * gate_time)\n",
    "print('Delay times for T2 echo test circuits:\\n', t2echo_delay_times)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 802.832x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Draw the first T2 test circuit\n",
    "t2echo_test_circuits[0].draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We'll create a noise model on the backend simulator\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "shots = 400\n",
    "\n",
    "# set the t2 decay time\n",
    "t2 = 25.0\n",
    "\n",
    "# Define the T2 noise model based on the thermal relaxation error model\n",
    "t2_noise_model = NoiseModel()\n",
    "t2_noise_model.add_quantum_error(thermal_relaxation_error(np.inf, t2, gate_time, 0.5), 'id', [0])\n",
    "\n",
    "# Execute the circuit on the noisy backend\n",
    "t2echo_backend_result = execute(t2echo_test_circuits, backend, shots=shots,\n",
    "                                       noise_model=t2_noise_model, optimization_level=0).result()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(t2echo_backend_result.get_counts(t2echo_test_circuits[0])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(t2echo_backend_result.get_counts(t2echo_test_circuits[len(t2echo_test_circuits)-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# T2 Decoherence Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0': [array([ 0.52316965, 28.330302  ,  0.47513291])]}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate the T2Fitter class using similar parameters as the T1Fitter\n",
    "t2echo_fit = T2Fitter(t2echo_backend_result, t2echo_delay_times, \n",
    "qubits, fit_p0=[0.5, t2, 0.5], fit_bounds=([-0.5, 0, -0.5], [1.5, 40, 1.5]))\n",
    "\n",
    "# Print and plot the results\n",
    "print(t2echo_fit.params)  \n",
    "t2echo_fit.plot(0)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circuits generated:  50\n",
      "Delay times:  [ 0.1  0.6  1.1  1.6  2.1  2.6  3.1  3.6  4.2  4.7  5.2  5.7  6.2  6.7\n",
      "  7.2  7.8  8.3  8.8  9.3  9.8 10.3 10.8 11.4 11.9 12.4 12.9 13.4 13.9\n",
      " 14.4 15.  16.  17.5 19.  20.5 22.1 23.6 25.1 26.6 28.2 29.7 31.2 32.7\n",
      " 34.3 35.8 37.3 38.8 40.4 41.9 43.4 45. ]\n",
      "Oscillating frequency:  0.08888888888888889\n"
     ]
    }
   ],
   "source": [
    "# 50 total linearly spaced number of gates \n",
    "# 30 from 10->150, 20 from 160->450\n",
    "num_of_gates = np.append((np.linspace(1, 150, 30)).astype(int), (np.linspace(160,450,20)).astype(int))\n",
    "\n",
    "# Set the Identity gate delay time\n",
    "gate_time = 0.1\n",
    "\n",
    "# Select the qubit to measure T2*\n",
    "qubits = [0]\n",
    "\n",
    "# Generate the 50 test circuits with number of oscillations set to 4\n",
    "test_circuits, delay_times, osc_freq = t2star_circuits(num_of_gates, gate_time, qubits, nosc=4)\n",
    "print('Circuits generated: ', len(test_circuits))\n",
    "print('Delay times: ', delay_times)\n",
    "print('Oscillating frequency: ', osc_freq)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('barrier', 3), ('h', 2), ('id', 1), ('u1', 1), ('measure', 1)])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 622.377x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(test_circuits[0].count_ops())\n",
    "test_circuits[0].draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('barrier', 8), ('id', 6), ('h', 2), ('u1', 1), ('measure', 1)])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224.38x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(test_circuits[1].count_ops())\n",
    "test_circuits[1].draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the backend to execute the test circuits \n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "\n",
    "# Set the T2* value to 10\n",
    "t2Star = 10\n",
    "\n",
    "# Set the phase damping error and add it to the noise model to the Identity gates\n",
    "error = phase_damping_error(1 - np.exp(-2*gate_time/t2Star))\n",
    "noise_model = NoiseModel()\n",
    "noise_model.add_quantum_error(error, 'id', [0])\n",
    "\n",
    "# Run the simulator\n",
    "shots = 1024\n",
    "backend_result = execute(test_circuits, backend, shots=shots, \n",
    "noise_model=noise_model).result()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the noisy results of the shortest (first in the list) circuit\n",
    "plot_histogram(backend_result.get_counts(test_circuits[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kgioKzYg4LSJO6/R5bERcFRFvqH5pkiTVlkr3NL9F6TmVRMQQoAX4FPBoRFxR5dokSaoplYZmE/CD8vuLgb3AaOBq4OYq1iVJUs2pNDSHAP9Wfv+7wD+UH+H1A+CsKtYlSVLNqTQ0/xV4Q0T8BrAQeKTcPgJ4vpqFSZJUayp9NNingXuB/cBmYGm5fT7wVBXrkiSp5lQUmpn5txGxEpgAPJKZL5UX/Rz4cLWLkySpllT8EOrMbKE0a7Zz27eqVpEkSTWq4psbRMR1EfF0RDwfEZPLbbdExH+ufnmSJNWOSm9ucCPwIeDzQHRa9AvgT6pXliRJtafSPc1rgasz8w6gvVN7KzCjalVJklSDKg3N3wJWd9P+a2DwsZcjSVLtqjQ0NwCN3bRfBKw59nIkSapdlc6e/Wvgzog4g9I5zeaI+APgvwJXVrs4SZJqSaXXaf5dRAwEPgGcQelGB78AbsjMB45DfZIk1YyjuU7zbuDuiBgFnJaZu6pfliRJtafi0DwoM5+tZiGSJNW6I4ZmRDwJLMjMPRHxFJA99c3MWdUsTpKkWlJkT/NBoK3T+x5DU5Kkk9kRQzMzP9rp/UeOazWSJNWwSm+j94OIeGU37cMi4gdVq0qSpBpU6c0NLgAGddN+OnD+MVcjSVINKzR7NiI63wVoVkQ81+nzAGAhsK2ahUmSVGuKXnLSQmkCUAIPd7P8BeD6ahUlSVItKhqakyjdNm8D8NvALzstOwDsysyOKtcmSVJNKRSambm5/Lbih1ZLknSyKHJzg4uB/5eZvy6/71Fm/t+qVSZJUo0psqf5dWAssKv8vidJaVKQJEknpSI3Nzitu/eSJJ1qDEFJkgoqek6zEM9pSpJOZkXPaRbhOU1J0kmtonOakiSdygxESZIK8jpNSZIK8jpNSZIK8jpNSZIKMgQlSSqo4tCMiMaIuCciWsqve7s8b1OSpJNSRaEZEe8Gfgy8Gvh2+TUGeCIi3lP98iRJqh1Fn6d50F8CH87MT3RujIg/Bz4OfLlahUmSVGsqPTz7KuCr3bR/DRh97OVIklS7Kg3NfwYu6Kb9AmDJsRYjSVItq/SG7d8B/ioimoAfldteB1wMfKTq1UmSVEOK7Gl+vdPrs8BI4Brg/5Rf1wCjysuOKCKui4iNEfFiRKyMiPN76bsgIh6PiN0R8UJE/DQibu6m3zsjYk1EtJX/fEeRWiRJqsQRQzMzTyv4OuLdgCLiUuAO4BPAucDjwHci4sweVtkPfAaYD0ynNNnooxFxXacxm4EHgK8As8t/fi0i5h6pHkmSKtHXNze4CVicmXdn5jOZeT2wHXh/d50zc2Vm3p+ZT2fmxsz8MvA9oPPe6Y3AP2fmX5bH/Evg0XK7JElVU+klJ0TEcOD3gDOBQZ2XZebHellvEHAe8NddFj0MvL7gzz633PcjnZqbefmh4e8Bf1JkTEmSiqooNCPidcC3gDZKl59so3SjgzZgE9BjaFI67zkA2NmlfSfw5iP83K3lnzcQ+Ghm3tVp8dgexhzbw1jXUDoPS319PY8++igAkydPZujQoaxatQqAkSNHMmPGDJYuXQrAwIEDmTdvHq2trezduxeApqYmdu7cCZzVW/k6QbW0tLB//34A5s6dy9atW9m2bRsA06ZNY8CAAaxZswaAsWPHMmnSJJYvXw7A4MGDmTt3LitWrOCFF14AoLm5mY0bN7Jjxw4Apk+fTkdHB2vXrgVg3LhxjB8/nhUrVgAwZMgQmpqaWL58OW1tbQDMmzePdevWsWvXLgBmzpxJW1sb69evB2DChAmMGTOGlpYWAIYNG0ZjYyPLli2jvb0dgPnz5/P000+ze/duABoaGti3bx8bNmwAYOLEiYwYMYLW1lYAhg8fTkNDA0uWLCEziQgWLFjAqlWr2LNnDwCNjY0899xzbNq0CTi279OWLVsAmDJlCnV1daxevRqA0aNHM3XqVJYtWwZAXV0dzc3NR/V7groK/zboRLB9+/aqfJ96E5lZuKCI+CHwL8CfAnuBBuDfgfuAL2bmV3pZt55SyC7IzKWd2m8F3p2Z03pZdxIwhNJM3f8B/Glm3ltedgC4KjPv6dT/CuDuzOz1m9HU1JQH/3E5FlfffsxDqAbdfWN/V6Djxe/syala39mIWJmZTd0tq/Tw7CzgjzIzI6IDqMvMDRFxC/D3lCbh9ORZoIPSbfc6GwPs6O2HZubG8tunImIMpcOz95bbdhzNmJIkVarSiUAHOr3fCfxW+f1+oL63FTPzALASuLDLogspzaIt6jQOP7ayvApjSpJ0RJXuabYCc4B1lGaofry85/ce4MkC638auDcingAeA66lFLZ3AUTEPQCZeUX58/XARmBtef35wM3A5zqNeQewNCI+CPwj8A7gjcC8CrdNkqReVRqa/w0YWn7/IeAeSjNX1wF/eKSVM/OBiBhZXvfVwGrgoszcXO7S9XrNAZTOYU4E2oGfAx+kHLLlMR+PiMsoXcP5sXKfSzNzRYXbJklSryoKzcxs6fT+l5QuPalIZn6Ow/cUOy+7oMvn24HbC4x58I5FkiQdNxVfpwkQEWcBry1/XJOZG6pXkiRJtanS6zRHAl8EFgEv/UdzfBO4MjN3V7k+SZJqRqWzZ78AnE3pNnanl1/zgUnA3dUtTZKk2lLp4dmFwJsyc3mntsci4r8A/1S9siRJqj2V7mn+ktIdgLp6HvDQrCTppFZpaH4MuD0ixh1sKL//G3q/76wkSSe8Ix6ejYingM43qJ0EbIqIbeXP44AXgdGUznlKknRSKnJO0+sfJUmiQGhm5kf7ohBJkmrd0d7c4HeA6ZQO2z6dmY9WsyhJkmpRpTc3GAf8A3Ae8Ityc31EtADvyMxf9LiyJEknuEpnz36G0jMxz87MCZk5AZhSbvtMtYuTJKmWVHp49kLggk4Phab8EOobgO9XtTJJkmpMpXuacPjlJ721SZJ0Uqk0NL8PfDYiJhxsiIgzKT2+yz1NSdJJrdLQvAH4DWBDRGyOiM2UHvr8G+VlkiSdtCo9p7kb+G3gAuA15bZnMtObtUuSTnqFQzMiBgC/Ahoy8xHgkeNWlSRJNajw4dnM7AA2A4OOXzmSJNWuSs9p/nfgtogYdTyKkSSpllV6TvNmSk852RYRW+nybM3MnFWtwiRJqjWVhubXKV2TGcehFkmSalqh0IyIM4BPAb8PvILSNZnXZ+azx680SZJqS9Fzmh8F3gd8C7gPeDPwv49TTZIk1aSih2cvBv4oM+8HiIivAI9FxIDyrFpJkk56Rfc0JwA/PPghM58A2oH641GUJEm1qGhoDgAOdGlr5ygfYi1J0omoaOgF8OWIaOvUdjpwd0Q8f7AhMxdVszhJkmpJ0dD8UjdtX65mIZIk1bpCoZmZf3i8C5EkqdYdzUOoJUk6JRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklSQoSlJUkGGpiRJBRmakiQVZGhKklRQn4dmRFwXERsj4sWIWBkR5/fS99UR8fcR8dOI6IiIxd30eV9EZDev04/rhkiSTjl9GpoRcSlwB/AJ4FzgceA7EXFmD6vUAc8CtwErehn6eeDVnV+Z+WK16pYkCfp+T/MmYHFm3p2Zz2Tm9cB24P3ddc7MTZl5Q2YuBp7rZdzMzB2dX9UvXZJ0quuz0IyIQcB5wMNdFj0MvP4Yhx8cEZsjYmtEfDMizj3G8SRJepmBffizRgEDgJ1d2ncCbz6GcdcCVwKrgKHAnwKPRURDZq7v2jkirgGuAaivr+fRRx8FYPLkyQwdOpRVq1YBMHLkSGbMmMHSpUsBGDhwIPPmzaO1tZW9e/cC0NTUxM6dO4GzjqF81aqWlhb2798PwNy5c9m6dSvbtm0DYNq0aQwYMIA1a9YAMHbsWCZNmsTy5csBGDx4MHPnzmXFihW88MILADQ3N7Nx40Z27CgdCJk+fTodHR2sXbsWgHHjxjF+/HhWrCidiRgyZAhNTU0sX76ctrY2AObNm8e6devYtWsXADNnzqStrY3160t/1SdMmMCYMWNoaWkBYNiwYTQ2NrJs2TLa29sBmD9/Pk8//TS7d+8GoKGhgX379rFhwwYAJk6cyIgRI2htbQVg+PDhNDQ0sGTJEjKTiGDBggWsWrWKPXv2ANDY2Mhzzz3Hpk2bgGP7Pm3ZsgWAKVOmUFdXx+rVqwEYPXo0U6dOZdmyZQDU1dXR3Nx8VL+n0pkfnWy2b99ele9TbyIzj+MmdPpBEfXANmBBZi7t1H4r8O7MnHaE9b8JPJuZ7ztCvwHAT4B/zswbeuvb1NSUB/9xORZX337MQ6gG3X1jf1eg48Xv7MmpWt/ZiFiZmU3dLevLc5rPAh3AmC7tY4CqnYPMzA6gBZhSrTElSYI+DM3MPACsBC7ssuhCSrNoqyIiAphFaYKRJElV05fnNAE+DdwbEU8AjwHXAvXAXQARcQ9AZl5xcIWImF1+Owx4qfz5QGauKS//C+BHwPpynxsohWa3M3IlSTpafRqamflARIwEPkTpesrVwEWZubncpbvrNf+ly+e3AZuBieXPrwQ+D4wFflXuPz8zn6hq8ZKkU15f72mSmZ8DPtfDsgu6aYsjjPcB4ANVKU6SpF5471lJkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpIENTkqSC+jw0I+K6iNgYES9GxMqIOP8I/ReU+70YERsi4tpjHVOSpKPRp6EZEZcCdwCfAM4FHge+ExFn9tB/EvDtcr9zgb8CPhsR7zzaMSVJOlp9vad5E7A4M+/OzGcy83pgO/D+HvpfC/wiM68v978b+BJw8zGMKUnSUemz0IyIQcB5wMNdFj0MvL6H1Zq76f89oCkiXnGUY0qSdFT6ck9zFDAA2NmlfScwtod1xvbQf2B5vKMZU5KkozKwvwvoaxFxDXBN+eP+iFjbn/WcgEYBz/Z3EX3hCx/o7wqkqvA7W7nf6mlBX4bms0AHMKZL+xhgRw/r7Oihf3t5vKh0zMz8PPD5wlXrMBHRkplN/V2HpGL8zlZXnx2ezcwDwErgwi6LLqQ047U7y3vo35KZvz7KMSVJOip9fXj208C9EfEE8Bil2bH1wF0AEXEPQGZeUe5/F/AnEXE78LfAG4D3AZcXHVOSpGrp09DMzAciYiTwIeDVwGrgoszcXO5yZpf+GyPiIuB/UrqE5BfADZn5YAVjqro8tC2dWPzOVlFkZn/XIEnSCcF7z0qSVJChKUlSQYamJEkFGZo6ooiYEhFdr4WVpFOOE4HUrYgYDfwB8AHgl5RuKLEd+DrwYGb+ez+WJ0n9wtBUtyJiMTAd+CawGxgJzAZeC2wFPpmZj/RXfZJeLiKGAfvSf9iPG0NTLxMRAeyjdL3r0k5t44HXAVdTujfjpZn5k/6qU9LhIuJvgSfKr82ZubebPiMzc3efF3eS8JymujMd2AgcONiQJVsy82vAf6IUqpf2U32SuoiIyyn9h/ZvgG8An4qId0TEWRExuNxnMPDFiDinH0s9obmnqZcpf7G+CZwBXAH8PDNf6tLneuCPMnN231coqauIuJvSAyw+CVwMvBc4C1gLfBv4PjANuCMzB/VXnSc69zT1Mpn5AvDfgMHAPcAVETEhIoYARMQZwAJKtyyU1M8iYiClo0P/lpkbMvOvM/McYA6whFKAfhX4LHBv/1V64nNPUz2KiJnAh4FFwL9TeurML4E3U5pJe1VmPtV/FUo6KCKGA2My86cRMQj4decJQRFxKXAf0OhchKNnaOqIypefvBX4feBFSnuYX8vMn/ZnXZJ6FxGnUfp3viMirqZ0aPaM/q7rRGZoqiIRcVrX85uSal9E3AQMyMxP9XctJzJDU5JOARHxCqDD//QeG0NTkqSCnD0rSVJBhqYkSQUZmpIkFWRoSpJUkKEpSVJBhqYkSQX9f5Tdiuh86x5RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the noisy results of the largest (last in the list) circuit\n",
    "plot_histogram(backend_result.get_counts(test_circuits[len(test_circuits)-1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'$T_2^*$ for qubit 0'}, xlabel='Time [micro-seconds]', ylabel='Ground State Population'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the initial values of the T2StarFitter parameters\n",
    "param_T2Star = t2Star*1.1\n",
    "param_A = 0.5\n",
    "param_B = 0.5\n",
    "\n",
    "# Generate the T2StarFitter with the given parameters and bounds\n",
    "fit = T2StarFitter(backend_result, delay_times, qubits,\n",
    "                          fit_p0=[0.5, t2Star, osc_freq, 0, 0.5],\n",
    "                          fit_bounds=([-0.5, 0, 0, -np.pi, -0.5],\n",
    "                                      [1.5, 40, 2*osc_freq, np.pi, 1.5]))\n",
    "\n",
    "# Plot the qubit characterization from the T2StarFitter\n",
    "fit.plot(0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mitigating Readout errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Qiskit classes\n",
    "from qiskit.providers.aer import noise\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "\n",
    "# Import measurement calibration functions\n",
    "from qiskit.ignis.mitigation.measurement import complete_meas_cal, CompleteMeasFitter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 575.449x385.28 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate the calibration circuits\n",
    "# Set the number of qubits\n",
    "num_qubits = 5\n",
    "# Set the qubit list to generate the measurement calibration circuits\n",
    "qubit_list = [0,1,2,3,4]\n",
    "\n",
    "# Generate the measurement calibrations circuits and state labels\n",
    "meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=num_qubits, circlabel='mcal')\n",
    "# Print the number of measurement calibration circuits generated\n",
    "print(len(meas_calibs))\n",
    "# Draw any of the generated calibration circuits, 0-31. \n",
    "# In this example we will draw the last one.\n",
    "meas_calibs[31].draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['00000',\n",
       " '00001',\n",
       " '00010',\n",
       " '00011',\n",
       " '00100',\n",
       " '00101',\n",
       " '00110',\n",
       " '00111',\n",
       " '01000',\n",
       " '01001',\n",
       " '01010',\n",
       " '01011',\n",
       " '01100',\n",
       " '01101',\n",
       " '01110',\n",
       " '01111',\n",
       " '10000',\n",
       " '10001',\n",
       " '10010',\n",
       " '10011',\n",
       " '10100',\n",
       " '10101',\n",
       " '10110',\n",
       " '10111',\n",
       " '11000',\n",
       " '11001',\n",
       " '11010',\n",
       " '11011',\n",
       " '11100',\n",
       " '11101',\n",
       " '11110',\n",
       " '11111']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. ... 0. 0. 0.]\n",
      " [0. 1. 0. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 1. 0. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "# Execute the calibration circuits without noise on the qasm simulator\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "job = execute(meas_calibs, backend=backend, shots=1000)\n",
    "\n",
    "# Obtain the measurement calibration results\n",
    "cal_results = job.result()\n",
    "\n",
    "# The calibration matrix without noise is the identity matrix\n",
    "meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel='mcal')\n",
    "print(meas_fitter.cal_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9MJPSringAjO7oKa+nYCrzWyPftq6Gviqmd0W328ETjWzGY0abWn0TElTgHPx7WwXmtlXI373FfiWxbuAD5vZyoiUeBm+NXIx8EEzmxf1fBrfnrcG+IRFrPD+6t+Q/s6Y0fBaJUlSEUmPDFyqaSw3s8nD2B7QQoWRpNHA+bgvyd2BoyTtjjud/Xcz2wV38nBCHHIC8Eyk/3uUI445Et+zPQX4tqTRDepPkqQLkFTp1QTqORdqSCvXPBs5Kb4qyvyAvljfU+M7lByoRvoVZrbCzB7G/Sju06D+JEm6gFGjRlV6NYFpwLGhdd8XeM76/J/WpZXT9v6cFL8FeNbcQWqRtkNteTNbLalwoLoDcEdNPcUx/dX/EsKMom1NKZIkeSlNGlUi6XLcg9g2kubjDoDGAJjZd3FHQe/EB2Yv4PGZBqSla57tQiweXwCDXohOkmQEaOKUHDM7aoB8A/7PYOttpfCs66RY0kYx+iyvLRTl59c4UG20HjHodYokSTqDZgnPVtFK4Tkd2FPSQ7jfv81xh7/7AQ+o78qcG+/XAj+MZAPuMDOTNA34laQT8DXaTXE/nQLeKmkR7u9xNXD0QJ3ae++962rVG/1YvWbSlSQjTbsLz1YqjAppI/q8g5fTyu9FXr30cprBusBSX8Qdq+4CXGlmM5vS8yRJRpxh1LYPiVZr2+8zs0lmtjMeCmIqrtTZLUySjgHeHeXfBRwT6bsB+5a07eeZ2c5m9ho8xvQ+AGZ2Nh6K9UEz+1ILzyVJkmFE0nBq24dEK1uuGxK4iradvnCl9epJkqSLafeRZ09o28umSjvuuOMApZMkaQd6ec2zoba9Jm298oPQtlfCzC4ws8lmNrne3twkSdqLdh95tlJ4NgoJfHiUOY6+UKb1wpVOA46UtEnsi98V17YnSdLFtLvwbNm0PXYJXYLH9hYuDGdK+iZwraTLcBOjU+KQHwIzJa3Ew5UeGulPAlvg4XyfA442szUAkq4HDgQ2krQU+KSZXbQBfa6bV+9HShOmJGk+hcKonWm1Y5CP4JrzzYHtwnHHycBHzWxj4Brgw3HIMcA1kf7RKAfuaupY4CTgR2Z2XamZrYC/iPO4DY9PnSRJF9DuI8+2dwxiZs+Hn72yvz7kzkrHmdkdMb2/rFRXkiQdTi8Lz2aZKjWqf34/9b8ESSdKmiFpxsKFCwd7HkmSjAC9LDzbhtS2J0lnUVVwdqvwbJapUqP6J/ZTf5IkXUAvC89mmSr1SzgrXSKp2MZ5bKmuplMvgl47/qhJ0g20+/bMtjdVkrQ1vp45Flgl6TDgYDObhe9zvxWPYfRtPHxokiRdQLsPQDrFVOkdwN/hUfEmhuAE+D6wI/CCmZ3caKSaJEnn0Otrni01VQIIM6UBY40kSdJ59LLwbLWpUmXSVClJOo9eFp5tQ5oqJUnn0e4Ko042VUqSpEvp9TXPlpoqtQv1TJjSjClJNoyeFZ6xblmYKj0PPBkxhr4JXBgmSe/C96QT5T4oDw38BeC0oi5JC4GLgJMkPR1aeyT9QNIqYHNJSyWd2arzSZJkeOlZ4TkEU6U5eDyiHwKfNrOHop6tgKXAtrgC6Vmg0LC/nvW9Kt3ZqvNJkmR46VnhySBNlcxsnpndh4cpLnMI8Esze9rMngF+CUxRelVKkq6m3YVnK2MY9Weq9BbqmyoNpp4dGKRXJTKGUZJ0DFIPO0NuJ9JUKUk6j3YfebaTqdJg60mvSknSxfSy8BysqVI9bgAOljRe0njgYOCG4faqNBTSjClJhk7PCs/BmirJt1CuxAXqJZJmRj1PAzfiHpgWADdGGvR5VVoDzCW9KiVJV9DTRvJDMFX6BTAZN1U63szeEPVshWvotwMmAAfECBTSq1KSdC09KzxpsalSHJNelZKkS+nlve2D9ao02Hoqo/SqlCQdRy+PPNuGNFVKks6ip9c8ab2pUpIkXUwvC8/pwJ6SHpI0B/gEbqp0J/BApP0QuBpA0tsl3Y2bHO1bqucG4MOSnpN0A2GqFMdMknQnsJmkH4dJVEeQZkxJ0phmCk9JUyTNljRH0mn95O8o6WZJ90i6T9I7B6qzlcKz0HwrXrVp5XeALfG1zDXAx2pMlc7HzZ32A84qmSpdB+wSn98F/LS5p5AkyUjRLIVRWP6cjweV3B04Kix/ypwOXGlmf4bbpH97wP4N+oyqsw9wn5lNMrOdgfNwbftbgN3MbBc86Nu7AcxsmplNAH5EyVQp8j4FHA3cbGaXAIRh/DbABDMbhQeJG9PC80mSZJho8ppnPcufMgaMi88vBx4fqNJOcAxSj62bWFeSJG3GIJaptpE0o/T9AjO7oPS9niwqcybwC0kfx+3SDxqo0VYKz7ZB6VUpSTqOQQjPRWY2eQObOwq41Mz+VdJbgR9K2sPMau3O19EJ2vZ6LK5aV5oqJUnn0cRpexWLnROAKwHM7HZgLL4sWJdOcAzSL7EVsyl1JUnSfjRReNaTRWX+BBwY7b4eF54Nd9S0vWOQyHsauAk4VNJ8SYdE1v3ApfK4R9vjcY46nqGYMSVJNyGpadr2kEUn4yaOD+Ba9ZmSzpL03ij2T8DfSvodcDnwkYF8ZbRszVPrOwaZD0zX+o5BrpD0XdwxyHfocwxyCjDNzK4qVXcEsBnwMTN7dyl9Gr4//tfACWa2olXnkyTJ8NLMQYGZXQtcW5N2RunzLOBtg6mzExyDYGY34kHgatPvMbN5Lel9kiQjSi/vMGqWY5ANRukYJEk6jl4Wnm1DatuTpLNospF8S2ilnWdDU6UYfaaTjyRJ+qXdFaEda6rUq6QzkaRX6FlnyA1Mlfo1L5L0ZklLca9KV0iaW9QlaTbwK+Cdkp4uTJUknSNpFfBqYJ6kC1t1PkmSDC/tPm0fiRhG0yLtEdY3L9oWuA0YjXtPWhT1bIU7/HgFsZ8d+G0c85fAX8R53Ar8pFXnkyTJ8NEJa57DbqrUwLxoKnCZOXfga6PbUyeGUeSNizhGhhvbH9bC80mSZBjpZeE52NhD9co3Sp9fpf40VUqSzqOXhWfbkKZKSdJ59KzCiMHHHqpXvlH6xEHUnyRJh9Dra55VPJmUmQYcK2df4LmIyX4DcLCk8ZLGEzGMIm+JpH3lV/BYetjsKWMiJd1GzwrPeqZKkv5L0mrcvOj+knnRtfia5UrcFvTcqOdp4EZgQbxuLMUw+j2uZV8DzMVjGiVJ0gX0rPBsYKr0ZTxo2yPAHmb20TjkUHzavTHwV3i0zcJU6QBgO2ACcECMQAG+D+wIvGBmJw/kQipJks6hZ4UnLTZVAggzpSdaeA5JkowQ7S48RyIA3GDKNzJVqowyhlGSdBSSRlSTXoX27l2TSFOlJOk82n3k2cmmSkmSdDG9LDxbaqrUwn53HWnGlHQiPSs8m2yqtAx3FDIfOKswVZL0PUkrgM0lvSDpC606nyRJho+qgrMrhWezTJWCT+JOP242s0tK6eOB48xMuGOQx1t0OkmSDDM9KzxpnqlSvwHg5Fet32BySZJ0Ph2/t13SZpI+K+n78X1XSe8e6Dia51WpHltTMZic0qtSknQU3TJtvwRYAbw1vj8GfLFlPWoBaaqUJJ1HNwjPnc3sbGAVgJm9AFTpcbNMleqxmAgmV7F8kiQdRLsLzyo7jFZK2hQwAEk74yPRgZgO7CnpIWAtrjQ6SNLJuALo1cBWRLgN3FTp/JLGfHmx9VLS9XhojueLys3MJP0JmC9pAr7m2bNelYZKI3cAjW7MdCOQtJp2N5erMvI8E7geeJWkH+Eejk6tcFzxdIm+karho8vN4/stJVMlqylb5pW456RtJM1XBIADzgCeBFbjmveLKvQrSZI2R7E9s50VRgOOPM3sF5LuAvbFBdvfm9miAQ4D17bfZ2ZFpMtP49r2U4FTJc0DJpfqmgqcZmaXR/nZkrY3syfMbE9J+wOnmNk6ZZWZXQ1cHXWVg8klSdLhdPzIU9KNZrbYzK4xs6vNbJGkGyvU3Wpte5IkXUzHrnlKGgtshk+Vx9M3nR5Hhwk1pVelJOk42n3k2Wja/jFcsfNK4C76hOcS4FsV6m61tr0yZnYBcAHA5MmTU9ORJB1AuwvPutN2MzvXzCbh64yvMbNJ8drLzKoIz2Y5BkmSpMfoBCP5Kgqjb0raA9gdGFtKv2yA41ZLKhyDCLipcAwCvA8YjTsGuTr2t88F/gw3a3oSeE9Rl6T78T3yoyQ9B3zAzG6QdCbwaXw//KOSLjezv6l++kkj0owpGUna3RnygMJT0ueA/XHheS3uwOM23BFHo+PKjkHmA9NLjkE+Bfya9bXtT+MC8zDgGTObUapnU+C1RT30KZZ2xx2DXCHpu8DvBj7lJEk6gY6dtpc4HDgQdyl3PLAX8PIKxw3KMYiZPWVm04mdTAPVk45BkqS7aea0XdKUMH+cI+m0OmU+IGmWpJmS/nOgOqvsMHrRzNZKWi1pHPAU6yt26jHYGEaDrWdQjkFIbXuSdAzNXM+M2ev5wDvomwVPM7NZpTK74kuAbzOzZyS9YqB6q4w8Z0jaEg/zexdwN3D74E9h5EjHIEnSeTRx5Nnv7LWmzN8C50eEXszsqYEqraIwOik+fjf2mI8zs/sqdLhZpkf16lnnGCRGn+kYJEm6iEEojLaRNKP0/YIwTyyoMgt+LYCk/8GV2Wea2fWNGq2iMLrRzA4EKNYqy2kNWGeqhAu1I4GjB2qvaj3hGORmfE32CuA40jFIknQNg5i2LzKzyRvY3EbArrhyfCLud+N/mdmz9Q6oK9oljZW0FbHDSNJW8dqJCjuMBhvDSNJ+8nhEnwO+EA5AxtWrJ5q5H7hUkgHbk45Bho2hBJVLkqo02c6zyix4PjDNzFaZ2cPAH3FhWpdG4+KP4Wucu8V78fpvKuww0uBjGP0Rdzv3ZeCzZjbRzJY0qAfcsH63qCsdgyRJF9FE4Vllw87/xUedSNoGn8Y/1KjSutN2MzsXOFfSx83sm1V6WMO6RdroUGGq9JX4XtveU8BTkt5VpR5glpnd019dSZJ0Ps16rmPDzsl4yPLRwMUxCz4LmGFm0+gLcT4Ld3/5KTNb3KjeRo5B3gw8WghOSccCf42P8s60CP/bgFabKlUmTZWSpPNo5qDIzK7FN/mU084ofTbgH+NViUbT9u/hMdSR9Hbgq/iuoucIJxudQpoqJUlnoQ53hjy6NLr8IK7+/wnwE0n3Vqi71aZKSZJ0Me2+HNdIbI9WX3C1A4GbSnlVdiYN1qtSq+tJhonBauHb/SFJRoYmKoxaQiPheTnwG0n/DbwI3AogaRd86t6QJpsqzQEeBJYCVxamSpJOk7Q86povqaGzkiRJOoeOFZ5m9iXgn4BLgf2sz8/YKODjA1XcLFOlyPsirnV/MPpVsBWuvBIeqC79fyZJl9DuwrPh9NvM7ugn7Y8V626WqRJmdksY59cylbDNwr0q/ZpqkT2TJGljRlowVqHK2uVQaZapUiMmlLzNPwlM6K9QmiolSefR7s6Q27t3gyCWFfp1YZ6mSknSebT7tL2VwnM4TIwWSNoeIN4HdCOVJEln0O7Cs9EOo6XUGckBmNm4AepullelRkzDvSl9lfSq1PZkTKSkKiMtGKvQSNu+RQjIc4HT8DXMibhC5hsDVdwsU6XIm4sHiHtDpJ8QzcwCTpd7VXofLkSTJOkCOnbkWeK9ZrZX6ft3JP0OOKPeATCkAHCFqdJheAC4r5eqOx5YBlxmZnuU0u8A9sa3kp5SYb99kiQdQjcojJ6X9CFJoyWNkvQhfCQ5EM0KAIeZ3YJH16xNf8DMZlfoS5IkHUa7jzyrCM+jgQ8AC+J1BNXWLvszVRrQiXIrkHSipBmSZixcuHAkupAkySCoKjjbetoeo8TaYEkdRcQzuQBg8uTJqX1Ikg6gYxVGBZJeK+lGSffH9z0lnV6h7vSGlCTJkGn3kWeVafv38XjGqwAicuaRFY6bDrxV0iJJM+OYafI4SL/Ep/BXSRoPIOc84BPAP0p6U1GRpOOAm3HTp+NK6XtL+j2+c+kUtftfVVKXocREyp+7u+kG4bmZmf22Jm31QAeFqdIXceXSLvR5Q7oSVyYJeBOudQdfRz0RX0rYArhd0jh5ELrzgU2jvxdJKhyTXIFvyRyFmyqVw48mSdKhqAOcIVdpeZGknQmDeUmHU9F7kZmdDfwl63tDehWwm5ltBLwOGBvpbweON7NxZrYFMA/3xnQI8CMz287MxuARMhfFjqI1ZvYKM9sEN4tK4ZkkXUK7jzyr2Hn+H1zZspukx4CHgQ9tQJv1nHnU0843Sp/fT/pLUDoGSZKOo92XZRoKzzB0P8nMDpK0OTDKzJY2q3Ezs9gd1FJS254knUe7C8+G03YzW4Pv+sHMnm+S4KznzKOedr5R+sR+0pMk6QLafdpeZc3zHknTJH1Y0vuL1wa0WTjzgPWdeUwDjg2t+77AczG9L+Ipjw/N/MHADZG3RNK+oWU/lnQMkiRdQVcYyeMKncXAAaU0A3460IFyhx47AaMkzcedfnwX+K084PxSYHIUvxb4Ch7ueDXwUQAze1rSjfjuJvD97cVWzd/jsZVGA98GrqtwPkmHkd6YepN239teZYfR8RtQ/0scekg6GzjHzL4q6TTgf+Oemg7Fp9174Xab5wI/ClOlA4DtcKF9l6TxZvYMboN6Oq7NP3kD+pkkSZvR7mueAwpPSZfQj19PM/ubgY6tE3uoXtyhqbiQNeAOSVvGmuj+wC+L0WYY2E8BLi9iLLX7RU6SZPC0+3NdZdp+denzWNwY/fENaLNZpkqVSVOlJOksRno9swpVpu0/KX+XdDlwWzMaT1OlJEnq0e7CcygrsrsCr9iANptlqpQkSRfT8dszJS2VtKR4AT9nw2KjN8VUaQPaT5KkA+h4U6XYZz4kmmiqtAxYBCwHTi4pj76H72nfWNILwL+a2WeH2t+k8xiKGVOaMLU/Iy0Yq1Bl5Pm22JqJpGMk/ZukV1es/3jgzcBMM5toZhcBH8NNlTYGzsFNlaDPVGlj4K9w13QFn8RjG91sZpeU0scDx5mZgMvYMEVWkiRtRLuPPKssGHwHeEHSXsA/4VEsL6tSeZ3YQ1NxEyXi/bBS+mXm3AEUpkqY2Y34KHUdsavoAOCqfupKkqTD6QbhuTpsL6cC3zKz83F/m0NlsKZK9dgaeDb8hjYsr4xhlCQdRzcIz6WSPg0cA1wjaRQwphmNh1AeFlMlM5tsZpO33XbbVjeXJMkGoi5xhvxBYAVwgpk9iZsKnbMBbQ7WVKkei/Gp/UYVyydJ0kE0c+QpaYqk2ZLmxLbweuX+WpJJmlyvTMGAwtPMnjSzfzOzW+P7n8ys0ppnHQZrqlSvX4bHNTq8n7qSJGMidTjNEp5yv8Tn40rp3YGjJO3eT7ktgL8H7qzSvyra9n0lTZe0TNJKSWskPVel8jBVmgu8QdJ8SSfgpkqfkrQS+BSukAI3VdoBN1W6GXcMUtTzNHATcGjUc0hk3Q9cKt+ltD0eoiNJki6giSPPfYA5ZvaQma3EY5/1F079C8DXcJPIAakybf8WcBTwIB6E7aO4+7cqNMtU6Qj8ZK+Legoj+WnAbsAj+LLCior9SpKkzRmE8NymUAjH68SaqgZURsuj9b7KzK6p2r8qjkEwszmSRodn+Usk3YOHIx7ouA32qmRmT5jZjZL2r6kHM7sH2n8PbJIkg2OQyyiLzGzANcoGbY0C/g3fcFOZKsLzBUkbA/fKfXE+wdD2xBcM1lSpUqTORii9KiVJx9FETfpAyugtgD2AX4fA3g6YJum9ZlY3Im+V3n04yp2Mx2B/FfDXg+p6HdJUKUmSejRxzXM6sKukSTEQPBJf8gPAzJ4zs23MbCcz2wm4A2goOKHa3vZHJG0KbG9mn6/S0wFYUEzHN9BUKUmSLqZZy3FmtlrSybhDodHAxWY2U+5fY4aZTWtcQ/9U0ba/B7gXuD6+v1FSpcYkXQzMAHYpJf8CuEXSg8AtRb3A3cC3Ja2QdB4lUyVJU/AtoX9VttGKf5I78en99+NfJUkaUs+EKc2Y2oeqo86qv4uZXWtmrzWznc3sS5F2Rn+C08z2H2jUCdWm7Wfiqv5no+J7gUmVegw74tPyTUqmSgb0d8Y/A34FvIBr10+CdTZaPwM2j/6eJelv45if4IJZuKu6myv2K0mSNqeZwrMVVFEYrTKz52o6WWmd0swOCm371aUAcLOBvyhN238dZZ8CjpB0JrCsJPn3AW4xs0Pi+E/jpgnChfOEGJa/FRf0SZJ0AR0fPROYKeloYLSkXXH7y/+3AW3W07bXoz8t/FsYhGOQJEk6i5EeVVahimj/OPAGfH/75cAS3L/mBjNc2nalV6Uk6Tg6ftpuZi8An4lXM6inba9HPS38OscgMfqsq53PAHBJ0nm0+8izrvAcSKNuZu8dYpuFY5CvUs2ZxzobLVw4HgkcHZE3C8cgV1SsK0mSDqFjhSfwVnyt8XLcy8igz0SDiGEkaT/gRnxv+3JJnwR2N7Mlki4BZkcfbjKzmdFE4Rjkclwo//1g+5gkZYYSE2mg45Kh0e7Cs9Ga53bAv+Dbls4F3oHvIf2Nmf2mYv2DcQzyR2A/4MvAZ6P8kjBV+gjuAGRzYDv1uZNKxyBJ0oWok50hm9kaM7vezI4D9gXm4Hs/T65a+WBiGJnZU2Y2HVhVU76uOykzu8fM5lXtT5IknUNHK4wkbQK8C3dJtxNwHm6wviE0y1SpMkrHIEnScbT7tL2RwugyfMp+LfB5M7u/2Y2H0mdYHIOQ2vYk6Sg6VnjiAd+ex5UwnyidiHC5N26IbTbLVClJki5lpKfkVagrPM2sVSuxTTFValHfkiRpE7phe+aQaaKp0hw8DMga4KzCVCk8LJ0JbALMl3SlmR3bynNKepc0Yxpe2n3k2WrRvsGmSpH3RVzr/mDhTirYCjjTzIQL0Q32Op8kSXvQ7tr2lgrPJpkq1aunbl1JknQ2zfbn2QpaOm2vw2BNlTa4rjRVSpLOo9en7Q1pplelRnVlDKMk6TzafeQ5EsJzQZgoUdFUabjqSpKkjejY7ZktpDBVgg33hNTMupIkaRN6es1THvztaDxaHWGqdDaufd9H0hnAb4H3S9oN+A9gb9zp8jpTJeAb/dTzOdwp83uA10k6CdfWf6BV55MkjRiKGVOaMDWml9c8LwX+HJhtZmPMbCK+O+h6M9sMOAu408yexjXpf4ebKZ1uZluWTJVeUk+YPN2Pa9t/A7zfzA6KupIk6QLafeTZMuHZajMlM3vAzGY3s89JkrQP7S48h9tUqZlmSpVJU6Uk6Tx6edrekOEK/hZtpalSknQQHe0MuUWkaVGSJJVo92n7cAvPNC1Keg4z6/fVjgKhnehZ4SkPyjYLeIOkVZJOwN3QfUjSCuArwPVRdrswQToN+FqUf1+pnrujntWSror090l6Ang7cIekJyRt3KrzSZJkeOlZ4WlmRwFTcNvN2WZ2kZktxm0x98RNjJZE2SeBg/EImZsBrwW+Lg/+dgzwDLBz5O0qaXcz+xlwKx6GeCN8FHtCq84nSZLhoxOM5Ifdq1IDE6OpwBVmtsLMHsYDzu1DnQBw8qt2AHBVHJ9elZKki0iFUXX6C/S2Q4P0rYFnzWx1TfpLkHSipBmSZixcuLDpHU+SpPn09MizXUhTpSTpPNpdeI6EP896NAr01l/6YmBLSRvF6DMDwyVJlzDSgrEKrY5hdDHwXuBlpbQj8JAZuwOvB2ZE1jTgV6GVHwVsijsOEbCnpIeAtcDmwEERtvhPeOyiCfiaZ5o+JR1DxkRqTLsLz1ZP23fEdxFtIml+CMbt8PVKA74h6YYoW9wRqvleTq/NOwPf5rkaGA9c1IqTSJJk+GnmtF3SFEmzJc2RB46szf9HSbMk3SfpRkmvHqjOVmvbD6ImAJyZfdPMtgNuAQ4xs0Oi+FTgPDPb2cxeA/yePm37fWY2ycx2Bs6LspjZ1Wb2Rny6foKZrWjl+SRJMnw0S9seJo/nA4fiM96jJO1eU+weYLKZ7Ylb8Jw9YP8GfUatY7Da9iRJupQm23n2a+5YLmBmN5vZC/H1DlyH0pB2Ep4tI02VkqTzGITw3KZ4vuN1Yk1Vgx2AnQBcN1D/OlnbXhkzuwC4AGDy5Mm9sdqeJB3OIBRGi8xscpPaPAaYDPzlQGXbaeQ5DThS0iaSJgG74tr26fiWzEmxd/3IKJskSRfTxGl7o4FZub2DgM8A762iP2mp8JQ0F5iLO/WYL+kESedIWoVL9hsKbbuZzcQ158vwPe4XmdmasOGcAzwILAWujLJIOk3ScuDVuMnSZa08nyQZLup5Yuolb0xNFJ4DDsAk/RnwPVxwVnKV2eqR5/HUaNuBi4E9cMcg67Ttof3aDrcJfR3wN6ElA/givuj7oJl9qVT/VsCZZibcdvQJkiTpeKTmOUOOAdjJwA3AA8QATNJZkt4bxc7BZc9/SbpX0oCz25aueZrZLZJ2qkl7APpdz1jnGAR4WFLhGOT2/uopHbN/fP4B8Gvg1CZ1P0mSEaSZI2kzuxa4tibtjNLngwZbZzuteQ7FJKlSTKTUtidJ59FMI/lW0E7Cc4NoFBMpHYMkSeeRwrM6lTRiNWRMpCTpQppsJN8S2kl41jNVGuiYjImUJF1IuztDbpnCSO5R6WhgdHyfD/wceBc+wlwFXCPp3tC4vxcfbS4FFgAnmtmaOvV8Dg/h8R7gdZJOAv6Ih/hIkq6mV7wxtbvpVSvF9qXAn+Pxi8aY2UTcqcc7cDOlPzezCWZ2SJgpHQm8EjdTWgn8ol49YfJ0P65t/w3wfjM7yMzWC/mRJEnn0u7T9paNPFttptSgriRJOpyRFoxVaJc1z5Z6TkpTpSTpPNp95NkuwrOlpKlSknQe7S4828Wr0lDMlJIk6WJGUpNehXbp3VDMlJIk6VJ62s5T0uXALNyj0iq5R6X3SVoMvB2YLul2WOdR6efAs7gXpvvNbE2pnulRz2pJv5LzPkmPRV13SFokaXyrzidJOoFu8sbUs8LTzI4CpgB742ZGF5nZz4D98KiZvwE+XjrkK7gZ00nAn2rqeRh4KzAGtw+dEnX9CPgXM9sI+DrwksBOSZJ0Jj0rPMHNlYCna9IeMLPZ/ZR93sxuA5aX02Pb5TgzuyP2r18GHBbZU3FvSsT7YSRJ0hW0u/BsF4VRI3bATZcKymZMlb0qAScC7Ljjji3qZpIkzaRdlxMK2kVhtMGkV6Uk6R7URGfIraIThOdjrB8GtGzGlF6VkqRLafdpe9sLz5iWL5G0r/xKHUuf96T0qpQkXUq7C8+28KoU5a7Cg8KtBFZLOgw4GNgUj1X0G+BF4D+A6yQdQXpVSpLKDMUb00h6YurlNc9LqehVCdewfxY3U7rQzF4W3pNmAd8BjgHGArcD18T6ZnpVSpIupROM5NvCq5KZPQ/cJmmXcnrZTCm+F2ZK16VXpSTpbtp9e2a7myo1MlOqTJoqJUnn0e4Do/YW7U0iTZWSpPPo2Wl7k2hkppQkSZcy0oKxCq10DHIxMAPYpZR2hKSZuFb99aX0rSXdDHwXd/QBrDNTGidpgaRllMyU5B6Yfgy8Bbi0dn01SZLqtKMzkXYfebZy2r4jvuNnE0nzJZ0AbAdsHenfkHRDlF0O7AasAXaL8rtH3r8AzwCb4R6Xrov083Czp1H4iPT2Fp5LkiTDTLsLz1Zq2w+K0eDVZrZHKeubkn4NnGJmM6Ls88D2kj4CTDazk0v1XIqPLJeV04GdgHeY2e2SNgKelCTrtBCBSZK8hGJ7ZjvT7muejVgX98jMVkt6Dh/VLhrRXiVJ0hTafc2zk4VnZdJUKUk6j3YXnu09Lm7MurhHMW1/ObC4v4JpqpQknUe7r3l2svAsOwU5HLgp1zuTpHvoWeEpaS6uHX9DoW2XdI6kVbip0g2Ftj1MlV4ELgFOLGvbJd0uaTWweaSfGU3MBj4oyYAvkCE4kqTpjFRMpJ7e2w4cDywDLiu07ZJeD1wIfI+Sth03VXoHsAewR41W/R+AR4AHw7lIwRxcCJ8CTDOzh1p4LkmSDDM9q21vhmOQyLujzjHzIn1tE7udJEmb0O4Ko9S2J0nSlrS78GzvcXGTSG17knQWzV7zlDRF0mxJcyS9RD9SbPeO/DurbPfuCeGZJEnn0SzhKWk0cD5wKLA7cFRp+3fBCcAzZrYL8O/A1waqN4VnkiRtSROjZ+4DzDGzh8xsJXAFHoWizFTgB/H5KuBADSCZ1SrTSEmXA/sD2wALgM8BTwPfBLYFngXujTAcSJoHjAM2jryDzWyWpLPxGEevBB7Hw3ScKenNwM+A8bi2/kkze0OFfi3EtfdE3+pt5xzOvOxH9qPb+vFqMxvyGpmk66POKozFZUDBBWZ2Qamuw4EpZvbR+P5h4C1lqx5J90eZ+fF9bpSpv927kR1Xt7+AGe2Ql/3IfnRzP0b6hW+iubD0/cPAt2rK3A9MLH2fC2zTqN6ctidJ0u2s28od9OdUvfJ274IUnkmSdDvTgV0lTZK0MXAkvr27zKC3e/eEnWcDLmiTvOxH9qOb+zGimLusPBm4ARgNXGxmMyWdhS83TAMuAn4oaQ6umzlyoHpbpjBKkiTpZnLaniRJMgRSeCZJkgyBFJ5JkiRDIIVnkiTJEEjhmSRJMgRSeCZJkgyBFJ5JkiRD4P8DFzWBh09IvVUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "meas_fitter.plot_calibration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 802.977x385.28 with 1 Axes>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a 5 qubit circuit\n",
    "qc = QuantumCircuit(5,5)\n",
    "# Place the first qubit in superpostion\n",
    "qc.h(0)\n",
    "# Entangle all other qubits together\n",
    "qc.cx(0, 1)\n",
    "qc.cx(1, 2)\n",
    "qc.cx(2, 3)\n",
    "qc.cx(3, 4)\n",
    "# Include a barrier just to ease visualization of the circuit\n",
    "qc.barrier()\n",
    "# Measure and draw the final circuit\n",
    "qc.measure([0,1,2,3,4], [0,1,2,3,4])\n",
    "qc.draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "least busy backend:  ibmq_ourense\n"
     ]
    }
   ],
   "source": [
    "# Obtain the least busy backend device, not a simulator\n",
    "from qiskit.providers.ibmq import least_busy\n",
    "# Find the least busy operational quantum device with 5 or more qubits\n",
    "backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 4 and not x.configuration().simulator and x.status().operational==True))\n",
    "# Print the least busy backend\n",
    "print(\"least busy backend: \", backend)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute the quantum circuit on the backend\n",
    "job = execute(qc, backend=backend, shots=1024)\n",
    "results = job.result()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Results from backend without mitigating the noise\n",
    "noisy_counts = results.get_counts()\n",
    "\n",
    "# Obtain the measurement fitter object\n",
    "measurement_filter = meas_fitter.filter\n",
    "\n",
    "# Mitigate the results by applying the measurement fitter\n",
    "filtered_results = measurement_filter.apply(results)\n",
    "\n",
    "# Get the mitigated result counts\n",
    "filtered_counts = filtered_results.get_counts(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(noisy_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(filtered_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.20.1</td></tr><tr><td>Terra</td><td>0.15.2</td></tr><tr><td>Aer</td><td>0.6.1</td></tr><tr><td>Ignis</td><td>0.4.0</td></tr><tr><td>Aqua</td><td>0.7.5</td></tr><tr><td>IBM Q Provider</td><td>0.8.0</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.8 | packaged by conda-forge | (default, Jul 31 2020, 02:25:08) \n",
       "[GCC 7.5.0]</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>31.40900421142578</td></tr><tr><td colspan='2'>Thu Sep 10 22:53:59 2020 UTC</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
